以人工智能为基础的模型从胸部放射图分类心脏功能:一个多机构的,追溯的模型开发和验证研究
Daiju Ueda1, Toshimasa Matsumoto1, Shoichi Ehara2
1Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan; Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan.
The Lancet. Digital health
|July 8, 2023
概括
一个深度学习模型现在可以通过胸部X射线检测膜心脏病并评估心脏功能. 这种人工智能工具为心声回声学提供了更快的替代方案,特别是在专家无法使用的地方.
科学领域:
- 医疗成像中的人工智能
- 心血管疾病的诊断 心血管疾病的诊断
- 放射学和心脏病学.
背景情况:
- 胸部放射是常见的成像方式.
- 从胸部X射线图中评估心脏功能和膜疾病是具有挑战性的.
- 现有的方法缺乏从胸部X射线对心脏参数的全面评估.
研究的目的:
- 开发和验证一个深度学习模型,通过胸部X射线图同时检测心脏膜疾病和心脏功能.
- 为了评估模型的性能与心声回声标准对比.
- 探索人工智能在心脏诊断中的潜力,使用广泛可用的成像.
主要方法:
- 一个深度学习模型被训练,验证,并使用来自多个机构的胸部X射线图和心声回声图进行外部测试.
- 该模型旨在对左心室喷射分数,三回速度和各种膜疾病进行分类.
- 使用接收器操作特征曲线 (AUC) 下的面积,精度,灵敏度和特异性来评估性能.
主要成果:
- 该模型在外部测试数据集中的各种心脏功能和膜疾病分类中实现了高性能.
- 对于左心室喷射分数,AUC为0.92,准确率为86%.
- 该模型在检测额头吐 (AUC 0.89) 和三角吐 (AUC 0.92) 方面表现强.
结论:
- 一个深度学习模型可以从胸部X射线图中准确地分类心脏功能和膜心脏疾病.
- 这种人工智能方法提供了一个快速的,低资源的替代方案,用于心声回声学.
- 该模型有可能改善心脏护理的可访问性,特别是在资源有限的环境中.
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